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Real-time 3D rendering using depth-based geometry reconstruction and view-dependent texture mapping

Published:24 July 2016Publication History

ABSTRACT

With the recent proliferation of high-fidelity head-mounted displays (HMDs), there is increasing demand for realistic 3D content that can be integrated into virtual reality environments. However, creating photorealistic models is not only difficult but also time consuming. A simpler alternative involves scanning objects in the real world and rendering their digitized counterpart in the virtual world. Capturing objects can be achieved by performing a 3D scan using widely available consumer-grade RGB-D cameras. This process involves reconstructing the geometric model from depth images generated using a structured light or time-of-flight sensor. The colormap is determined by fusing data from multiple color images captured during the scan. Existing methods compute the color of each vertex by averaging the colors from all these images. Blending colors in this manner creates low-fidelity models that appear blurry. (Figure 1 right). Furthermore, this approach also yields textures with fixed lighting that is baked on the model. This limitation becomes more apparent when viewed in head-tracked virtual reality, as the illumination (e.g. specular reflections) does not change appropriately based on the user's viewpoint.

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References

  1. Nakashima, Y., Uno, Y., Kawai, N., Sato, T., and Yokoya, N. 2015. Ar image generation using view-dependent geometry modification and texture mapping. Virtual Reality 19, 2, 83--94. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Zhou, Q.-Y., and Koltun, V. 2014. Color map optimization for 3d reconstruction with consumer depth cameras. ACM Trans. Graph. 33, 4 (July), 155:1--155:10. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Real-time 3D rendering using depth-based geometry reconstruction and view-dependent texture mapping

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          cover image ACM Conferences
          SIGGRAPH '16: ACM SIGGRAPH 2016 Posters
          July 2016
          170 pages
          ISBN:9781450343718
          DOI:10.1145/2945078

          Copyright © 2016 Owner/Author

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 24 July 2016

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